Germplasm is the foundation of agriculture and a national strategic resource. The quality of seeds affects the production scale and potential of the overall planting industry. There are some problems in Chinast seed industry, such as incomplete and opaque industrial chain information and imperfect and unsafe seed traceability systems. This paper integrates blockchain technology into traditional seed information traceability and proposes a seed quality traceability model for distributed data storage. This paper uses Ethereum, Internet of Things, and terminal applications to design a threelayer architecture: application layer, Internet of things layer, and data storage layer. According to the characteristics of Ethereum and the requirements of system data storage and query, a distributed multidatabase storage mode is designed. The system stores the data information of each link from seed production to the circulation department. Combined with the characteristics of blockchain data information that cannot be tampered with, high transparency and traceability, it ensures that all data in the seed supply chain will not be changed or hidden by others to realize accurate traceability and rapid identification of seed quality. The system uses the smart contract algorithm to directly hit the source of the seed data to ensure the quality and safety of the purchased seeds and to provide information technology and service guarantees for safeguarding the rights and interests of seed consumers.

Fruit and vegetable picking is an important part of agricultural production, and it relies heavily on manual labor. The development and application of picking robots will greatly reduce labor dependence of picking operations. The picking robotic arm is a key part of the picking robot and a major focus of research on the picking robot. This paper classified the picking manipulators by the degree of freedom, and summarized the development process and research status of the picking manipulators in China and the rest of the world. Aiming at the problem of lack of standardized solutions in thechoice of freedom and configuration of the picking robotic arm for the same fruit and vegetable in the same cultivation mode, it is proposed that indepth integration of the picking manipulator research and agronomy is the key to solving the problem in future. At the same time, considering that the rigid body is difficult to adapt to the picking environment as well as the joint drive mode being single, it is believed that the flexible design of the picking manipulator body and the combined use of the drive mode will be the future development trend.

Yam planting in China has a long history and a wide range. Due to its own growth characteristics, geographical environment and planting mode, the mechanization degree of yam harvesting is relatively low, and manual harvesting is mostly used. With the development of industrial structure, the development degree of yam harvesting mechanization directly affects the economic benefits of farmers. This paper analyzes the planting characteristics of several yams in China, summarizes the main harvesting methods of several yams in China, expounds the research status and progress of main harvesting equipment and technology of yams such as walking system, transmission system, trenching system, soil turning vibration mechanism and positioning system, and briefly discusses the research on yam harvester in foreign countries. On this basis, it is pointed out that the mechanization of yam harvesting in China faces problems such as insufficient integration of agricultural machinery and planting environment, inconsistent planting mode, poor quality of harvesting machines and tools, lack of innovation and imperfect harvesting function, and relevant suggestions are put forward for the problems faced by the mechanized harvesting equipment of Yam as follows: developing the mechanized harvesting equipment of yam according to local conditions. Promoting the construction of planting regulations, learning from the development experience of developed countries, improving the innovation ability of core components, and developing combine harvesters.

 White radish is an important economic vegetable in China. It has the advantages of short growth cycle, strong adaptability and high yield. Based on the growth characteristics and planting patterns, a pulling white radish combine harvester is designed. The harvester is mainly composed of cherry blossom pulling device, soil ripping device, clamping device, rhizome separation device and collection devices, which can perform the combined harvesting operation including lifting, scarifying, conveying, cutting and collecting in series. In order to ensure the smooth entry of white radish stems and leaves into the gripper and avoid the stems and leaves from being pulled off, a lifter with conical opening is designed. By adjusting the conical opening angle of the lifter through active and underactuated combination, the adaptability of the prototype is improved under the condition of low planting line straightness and uneven ridge surface. In order to realize stable flexible pulling and gripping conveying, and avoid stem and leaf fracture in the process of pulling and conveying, a flexible gripper and gripper force adjusting device is designed. The gripper belt is composed of rubber in which the antiskid pattern is printed on the working surface. According to the growth environment, soil mechanical properties and operation stress characteristics of white radish, the structural parameters of the ripper are designed. Through finite element analysis and strength check, the performance and reliability of ripper are improved. To avoid the cutting damage caused by the shaking of white radish during the cutting process, and achieve the effect of smooth cutting and neat incision, a double disc cutter is designed. The field performance tests indicate that the working performance is stable, with the damage rate of 2.7%, the missed rate of 1.8% and the efficiency of 0.08~0.26hm2/h. The popularization and application of pulling white radish combine harvester will effectively reduce labor intensity and improve harvest efficiency, which is helpful for improving the mechanization level of white radish in China.

With the continuous development of lotus root industry in China, the mechanized harvesting technology of lotus root has been paid more and more attention in China. The development of reliable lotus root harvesting machinery has become the most important part of the modernization process of lotus root industry in China. Therefore, this paper summarized and analyzed the characteristics and harvesting requirements of lotus root plants, compared and analyzed the lotus root harvesting machinery at home and abroad, summarized the types, advantages and disadvantages of lotus root harvesting machinery in China, listed the representative research and development achievements of lotus root harvesting machinery in China, and pointed out that the planting mode of lotus root was not uniform in China. The problems of low degree of automation and single mechanical structure and function of lotus root harvesting machinery were put forward. Combined with Chinas national conditions, some development suggestions were put forward, such as standardizing the regional standardized planting mode of lotus root, formulating the standard requirements of lotus root harvester and increasing the research on key technologies of lotus root harvesting machinery, so as to provide reference for the further development of lotus root harvesting machinery.

In order to improve the monitoring level of facility agricultural environment, a remote wireless monitoring system for agricultural greenhouse environment based on ESP32 module was designed. The system comprised of environmental information acquisition module, ESP32 control module, output module, and smartphone control terminal.The monitoring terminal APP was designed with blinker Internet of Things solution based on Android platform. Greenhouse environment temperature, humidity, light intensity, PM2.5 concentration, CO2 concentration, access control and other environmental information was collected through sensors and sent to the remote monitoring mobile phone via WiFi network.The environmental information of the greenhouse was viewed and the relevant greenhouse equipment was controlled remotely using a mobile APP. The system physical prototype was manufactured and an experiment was carried out. The results showed that the lowest success rate of data transmission of the terminal was 97.3% and the average networks packet loss rate was 2.17%. The system has many advantages such as stable operation low cost,good real⁃time, high communication security,high reliability and easy operation.

The identification of crop disease is related to crop yield and quality, and it is an essential part in the development of intelligent agriculture. With the rapid development of deep learning in the field of image processing, the method of identifying crop disease types from images by deep learning has gradually become the mainstream. In this paper, we mainly review the methods of crop disease recognition based on deep learning, briefly introduce deep learning and convolutional neural network, and collect some common public disease image datasets. According to the different collection environment of training sample, we summarize the progress of deep learningbased disease identification methods in recent years from two aspects of laboratory and field, point out their advantages and disadvantages of each method, and conclude that there are three main problems in this research field such as insufficient data, difficult task and complex network structure of deep learning model. On this basis, we propose that the establishment of largescale, multispecies, and multitype disease database and the design of highperformance deep learning model are important development directions in the future.

Intelligent field weeding robots are an important manifestation of the modernization, refinement and intelligence of contemporary agricultural development, and are of great significance to national social development and environmental protection. In order to clarify the current research status of key technologies and equipment of intelligent weeding robots, the research status and operation modes of typical weeding robots were summarized from the aspects of weeding modes, identification and positioning of seedlings and grasses, and intelligent navigation modes. Then the importance and research progress of the key technologies of intelligent navigation, seedling and weed identification and weeding execution system were summarized. Combined with the research characteristics of weeding robot, such as complex and changeable operation environment, delicate operation object, special use object and seasonal operation, the existing problems of current key technologies were pointed out. According to the problems, the future development trends of integrated navigation technology, image processing technology of complex field environment, weed classification, research and development of interplant weeding terminal actuator and mechanical structure optimization are explained. 

With the advent of the era of agricultural intelligence, the application of robot technology in the field of intelligent picking in agriculture and fruit industry is becoming more and more extensive. As the core technology of picking operation, the advanced degree of path planning algorithm plays a decisive role in the efficiency and quality of picking operation. Therefore, according to the characteristics of path planning algorithm of picking mechanical arm, it can be divided into four categories such as swarm optimization algorithm, graph search algorithm, probability map algorithm (PRM) and rapidly expanding random tree algorithm (RRT), and based on the above classification, the representative research achievements of domestic and foreign scholars in recent years are introduced, the advantages and disadvantages of various path planning algorithm applied in picking operations are analyzed, and the feasibility, applicability and limitations of various algorithms are systematically summarized by comparison. Finally, the future development direction of path planning algorithm of picking mechanical arm is prospected, and specific suggestions on algorithm optimization are put forward, providing some ideas for path planning research of picking mechanical arm.

In order to solve the problem of poor uniformity of sowing operation, a wheat mechanized sowing control system based on STM32 single chip microcomputer is designed. The wheeled driving robot could travel with variable speed. According to the real-time driving speed of the wheeled driving robot, the speed of the seeding motor is controlled to realize the variable speed and uniform seeding. The system used multi-stage control of DC motor speed. The real-time speed signal of the wheeled driving robot is used for The first-stage control parameter, which is controlled by PID. The real-time current and speed of the seeder motor are used for secondary control parameters, which is controlled by fuzzy PID. Simulation results show that the control algorithm has short response time, small overshoot and good control effect. The results of sowing experiment have shown that the control accuracy of seeding amount under constant speed condition is 96.8% and that under variable speed condition is 95.1%.

 Under the background of global trade liberalization, it is of great significance to explore its impact on Chinas agricultural exports from the perspective of trade facilitation to enhance Chinas agricultural export competitiveness. This study selected the panel data of agricultural products exports between China and 10 ASEAN countries from 2010 to 2019 and established four primary indicators such as infrastructure, customs environment, regulatory environment, and ecommerce, as well as aviation infrastructure, customs procedures, and 17 secondary indicators such as policy transparency are used to construct the evaluation index system of trade facilitation. The article used the extended gravity model to analyze the primary influencing factors of trade facilitation empirically. The results showed obvious differences in trade facilitation among ASEAN countries, and there was still much room for improvement. The infrastructure efficiency, customs environment, and regulatory environment in the primary indicators of trade facilitation in ASEAN had a significant positive effect on agricultural exports. In contrast, ecommerce had no significant impact on Chinas agricultural exports. Based on the empirical results, the study proposed that ASEAN trading countries should strengthen infrastructure construction, optimize the construction of customs and institutional environment, and improve domestic Internet technology and penetration rate from the perspective of development indicators of trade facilitation to promote the improvement of their trade facilitation level. At the same time, it also put forward from the perspective of China that different development countermeasures should be taken for countries with different development levels of ASEAN trade facilitation in order to promote Chinas agricultural exports.

There are some problems in sorting clementine in our country, such as great difference in fruit size, low sorting efficiency, high manpower cost and low automation level. In order to solve the automatic sorting mature clementine, this paper introduces a machine which uses machine vision technology to identify, detect and sort mature clementine. The machine is composed of MCU module, vision recognition module, Stepper Motor Drive Module, actuator module, humancomputer interaction module, LCD module. Firstly, the machine vision module OpenMV is used to get the color image of the fruit, and the image is binarized. Secondly, the threshold editor in the OpenMV IDE software is used to set the LAB value representing the orange color of the mature sugar orange fruit. The SCM can judge whether the color of the sugar orange fruit is orange through calculation and comparison. Finally, the diameter of clementine is calculated by the relationship between the pixel and the diameter. The results show that the device can automatically identify the color, size and sorting of the target fruit. When the sorting rate is set at 120 fruits per minute, the error of the fruit diameter is less than 1.6 mm, the accuracy is 96.19%, the accuracy of classification and sorting is 95.4%, and the detection rate of unqualified fruits is 97.2%.

Chinas tea industry is still a laborintensive industry. Among them, fresh leaf picking consumes a lot of labor, which is the most laborintensive and labortime link in tea production. The mechanized tea harvesting is the only way for my countrys tea development. Starting from the current situation of tea picking in my country, this paper summarizes the tea picking standards and agronomic requirements of machine picking in China. The research and application status, advantages and disadvantages of single tea picking machine, double tea picking machine, riding tea picking machine and tea picking robots are analyzed from the perspective of onetime picking principle and selective picking principle respectively. The problems that restrict the realization of comprehensive mechanized tea picking in my country are analyzed,and the development opinions such as the deep integration of agronomy and agricultural machinery, the combination of basic research and advanced technology, and the improvement of the versatility of tea machinery are proposed in the future related to tea production in China, so as to provide reference for the further research of tea picking equipment in China.

Visionbased detection of agricultural pests is an important technology for achieving automated and realtime monitoring of pest conditions. Firstly, this paper introduces the application of traditional machine learning techniques in the visionbased detection of pests in China and internationally. Then, this paper summarizes the research progress of the new generation of visionbased detection methods for pests, which are based on deep learning techniques such as R-CNN, Fast R-CNN, Faster R-CNN, SSD, and YOLO. Next, this paper analyzes the problems that exist in the research and practical applications of visionbased detection methods for agricultural pests. The traditional machine learningbased methods have problems such as low feature capture ability, detection accuracy, and robustness, as well as high resource consumption. The deep learningbased methods have higher detection performance than the traditional machine learningbased methods but have problems such as poor performance on small and differently distributed targets, low detection accuracy, and slow speed. Finally, this paper discusses possible research directions in the future for the visionbased detection of agricultural pests based on deep learning techniques, including the development of public resources for agricultural pest image data, robust handling for data distribution shifts, deep feature learning, and multiscene applications.

In order to realize the mechanization and automation of strawberry picking in greenhouse, a strawberry picking robot used in solar greenhouse was designed and manufactured. The robot can realize autonomous path planning, identify mature strawberries and complete picking during walking. This design uses ROS distributed computing system as the main control network, uses laser radar to build and locate the map of mobile robot, binocular depth camera to identify and locate mature strawberries, and a 6-DOF mechanical arm equipped with flexible bionic grippers to grasp and place target strawberries. This paper also designs a robot software platform， using improved A* algorithm to achieve autonomous path planning and navigation obstacle avoidance; The R-FCN target detection network and binocular vision technology were used to detect and locate mature strawberries. The experimental results show that: the strawberry picking robot can achieve target detection and positioning, the error between the detected strawberry coordinates and the robots paw coordinates is less than 4 mm, and the recognition rate of mature strawberries is 95%, meeting the picking requirements.